Maize

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## ── Attaching packages ───────────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.2.0     ✔ purrr   0.3.2
## ✔ tibble  2.1.3     ✔ dplyr   1.0.2
## ✔ tidyr   0.8.3     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0

## Warning: package 'dplyr' was built under R version 3.6.2

## ── Conflicts ──────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

## 
## Attaching package: 'psych'

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

SIAP <- read.csv("/Users/erikaluna/R\ Studio/msc_thesis/SIAP.csv") 

one_crop <- SIAP %>% 
  filter(crop == "maiz") %>% 
  group_by(year, state) %>% 
  summarise(ag_yield = round(sum(production)/sum(harvested), digits = 2),
            ag_prod = sum(production),
            ag_planted = sum(planted),
            ag_harv = sum(harvested), 
            ag_losses = sum(losses))

## `summarise()` regrouping output by 'year' (override with `.groups` argument)

one_crop %>% 
  DT::datatable()

## Number of observations

## `summarise()` ungrouping output (override with `.groups` argument)

## Panel data A data frama with all years and all states that grow maize.

period <- tibble(rep(c(1980:2016), times = 32)) #32 states report maize production
colnames(period) <- c("year") 
states <- tibble(rep(c("aguascalientes","baja california","baja california sur",
                       "campeche", "chiapas", "chihuahua","coahuila","colima",
                       "distrito federal","durango",
                        "guanajuato", "guerrero", "hidalgo", "jalisco", 
                       "mexico", "michoacan", "morelos", "nayarit","nuevo leon", 
                       "oaxaca", "puebla", "queretaro", "quintana roo",
                        "san luis potosi", "sinaloa", "sonora", "tabasco", 
                       "tamaulipas", "tlaxcala","veracruz", "yucatan", "zacatecas"), times = 37))
colnames(states) <- c("state") 
states <- states %>% 
  arrange(state)
states_period <- cbind(states, period)

Data frame for Maize

maize <- left_join(states_period, one_crop, by=c("state", "year"))
maize <- maize %>%  
  transform(i=as.numeric(factor(state))) %>% 
  transform(t=as.numeric(factor(year))) %>% 
  group_by(year) %>% 
  arrange(state) 

maize %>% 
  DT::datatable()

Descriptive Statistics

describe(maize$ag_yield)

##    vars    n mean   sd median trimmed  mad  min   max range skew kurtosis
## X1    1 1116 3.48 3.29   2.49    2.92 1.56 0.26 39.87 39.61 4.53     35.9
##     se
## X1 0.1

summary(maize)

##     state                year         ag_yield         ag_prod       
##  Length:1184        Min.   :1980   Min.   : 0.260   Min.   :     25  
##  Class :character   1st Qu.:1989   1st Qu.: 1.670   1st Qu.: 105683  
##  Mode  :character   Median :1998   Median : 2.485   Median : 359930  
##                     Mean   :1998   Mean   : 3.484   Mean   : 737542  
##                     3rd Qu.:2007   3rd Qu.: 4.320   3rd Qu.: 979578  
##                     Max.   :2016   Max.   :39.870   Max.   :8337076  
##                                    NA's   :68       NA's   :68       
##    ag_planted          ag_harv           ag_losses            i        
##  Min.   :     6.5   Min.   :     6.5   Min.   :     0   Min.   : 1.00  
##  1st Qu.: 61926.8   1st Qu.: 46913.2   1st Qu.:  2682   1st Qu.: 8.75  
##  Median :162533.1   Median :138180.0   Median : 16725   Median :16.50  
##  Mean   :263319.8   Mean   :231465.7   Mean   : 32226   Mean   :16.50  
##  3rd Qu.:465139.0   3rd Qu.:405935.0   3rd Qu.: 44643   3rd Qu.:24.25  
##  Max.   :988367.2   Max.   :960143.8   Max.   :416498   Max.   :32.00  
##  NA's   :66         NA's   :67         NA's   :83                      
##        t     
##  Min.   : 1  
##  1st Qu.:10  
##  Median :19  
##  Mean   :19  
##  3rd Qu.:28  
##  Max.   :37  
## 

ggplot(maize) +
  aes(x = ag_yield) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 68 rows containing non-finite values (stat_bin).

ggplot(maize) +
  aes(x = ag_yield) +
  geom_density()

## Warning: Removed 68 rows containing non-finite values (stat_density).

maize %>%
  group_by(state) %>%
  summarize(max_yield = max(ag_yield,  na.rm=TRUE),
            min_yield = min(ag_yield,  na.rm=TRUE),
            range_yield = max(ag_yield,  na.rm=TRUE) - min(ag_yield,  na.rm=TRUE),
            sd_yield = sd(ag_yield,  na.rm=TRUE),
            mean_yield = mean(ag_yield,  na.rm=TRUE),
            median_yield = median(ag_yield,  na.rm=TRUE)) %>% 
  knitr::kable()

## `summarise()` ungrouping output (override with `.groups` argument)

state	max_yield	min_yield	range_yield	sd_yield	mean_yield	median_yield
aguascalientes	39.87	2.00	37.87	7.2798377	11.8245714	10.59
baja california	38.54	0.98	37.56	8.5605573	7.2329032	3.65
baja california sur	7.41	2.64	4.77	1.3394085	5.2271429	5.24
campeche	2.74	0.58	2.16	0.5573117	1.5691429	1.56
chiapas	2.56	1.45	1.11	0.2942522	1.9654286	1.90
chihuahua	9.97	0.57	9.40	2.4569107	5.1268571	4.85
coahuila	18.86	1.07	17.79	3.7133300	5.1442857	4.61
colima	10.51	1.76	8.75	2.1389538	4.0705714	3.12
distrito federal	11.00	1.97	9.03	1.4986115	2.7760000	2.48
durango	12.23	1.14	11.09	2.4056917	3.8565714	3.48
guanajuato	5.90	1.56	4.34	1.3113085	3.1977143	2.76
guerrero	3.06	0.97	2.09	0.4969693	2.2025714	2.26
hidalgo	3.46	1.56	1.90	0.5767255	2.2722857	2.21
jalisco	10.60	3.16	7.44	2.0650197	6.1022857	5.63
mexico	6.51	2.55	3.96	1.0881238	4.3457143	4.56
michoacan	4.15	1.46	2.69	0.7386203	2.5660000	2.31
morelos	3.50	0.91	2.59	0.7066578	2.3077143	2.10
nayarit	8.78	1.91	6.87	1.6842245	3.7422857	2.95
nuevo leon	11.06	1.00	10.06	2.2887925	3.3737143	2.64
oaxaca	1.54	0.92	0.62	0.1389323	1.2651429	1.26
puebla	2.79	1.13	1.66	0.3887487	1.9354286	1.99
queretaro	15.83	0.95	14.88	3.1923000	4.7117143	3.86
quintana roo	0.99	0.26	0.73	0.1616054	0.6131429	0.60
san luis potosi	1.75	0.81	0.94	0.2563096	1.2337143	1.18
sinaloa	10.54	1.03	9.51	3.0795467	5.7425714	6.14
sonora	7.05	2.77	4.28	1.1458762	4.6897143	4.69
tabasco	2.35	1.19	1.16	0.1957906	1.6331429	1.62
tamaulipas	4.94	1.56	3.38	0.9109864	2.9554286	2.74
tlaxcala	5.66	1.69	3.97	1.3930423	3.4051429	2.69
veracruz	2.44	1.47	0.97	0.2583350	1.8357143	1.84
yucatan	1.71	0.49	1.22	0.1963811	0.8885714	0.89
zacatecas	8.98	0.74	8.24	1.7751668	2.0965714	1.34

Plots

Production

maize %>% 
  group_by(state) %>% 
  summarise(max_prod = max(ag_prod, na.rm=T),
            min_prod = min(ag_prod, na.rm=T),
            range_prod = max(ag_prod, na.rm=T) - min(ag_prod, na.rm=T),
            sd_prod = sd(ag_prod, na.rm=T),
            mean_prod = mean(ag_prod, na.rm=T),
            median_prod = median(ag_prod, na.rm=T)) %>% 
  knitr::kable()

## `summarise()` ungrouping output (override with `.groups` argument)

state	max_prod	min_prod	range_prod	sd_prod	mean_prod	median_prod
aguascalientes	1493778.03	120108.00	1373670.03	421743.447	752373.75	715440.57
baja california	68442.00	25.00	68417.00	16092.783	18277.56	13658.00
baja california sur	97642.00	6634.00	91008.00	27614.561	37919.32	31540.65
campeche	464714.94	12839.00	451875.94	135606.273	175362.01	133041.15
chiapas	2135550.08	983415.00	1152135.08	282788.096	1461322.34	1460524.00
chihuahua	2319376.63	238326.00	2081050.63	550629.484	1211653.85	1125302.76
coahuila	937497.77	21696.96	915800.81	193974.135	184256.92	132786.00
colima	145185.85	46313.30	98872.55	21054.258	83773.46	77459.00
distrito federal	50161.00	9515.15	40645.85	9107.056	22218.15	23383.00
durango	2773265.28	194100.43	2579164.85	530611.100	689347.17	496439.00
guanajuato	2337499.61	383021.00	1954478.61	472100.685	1033940.03	897201.00
guerrero	1428121.17	331411.00	1096710.17	269719.455	1017910.16	1038965.35
hidalgo	879327.76	233929.00	645398.76	154093.490	519376.35	513798.00
jalisco	8337075.94	2420683.00	5916392.94	1453557.271	4451739.90	4053390.00
mexico	3610125.45	1037491.00	2572634.45	574154.477	2602864.79	2600755.36
michoacan	1935286.73	606327.00	1328959.73	340786.696	1173786.27	1130893.00
morelos	122714.05	29214.00	93500.05	20841.622	85761.65	90722.50
nayarit	439237.43	103011.00	336226.43	78919.814	219908.24	208767.21
nuevo leon	880313.40	30372.90	849940.50	176381.775	181841.54	128472.00
oaxaca	851011.00	220535.00	630476.00	164203.079	607910.18	645255.35
puebla	1417706.20	522662.00	895044.20	227714.433	1005639.22	1074593.00
queretaro	1035148.30	58909.00	976239.30	321939.078	423422.56	335106.00
quintana roo	67469.92	4160.32	63309.60	16919.277	31771.94	33769.56
san luis potosi	256366.00	68751.00	187615.00	39217.713	167479.30	172358.00
sinaloa	6462324.35	64754.00	6397570.35	1910634.563	2297744.80	2449096.00
sonora	841582.00	46513.00	795069.00	179725.857	235612.83	179494.74
tabasco	181556.66	53710.00	127846.66	29035.967	106481.81	104467.40
tamaulipas	1356220.00	153360.81	1202859.19	285901.402	612883.77	557779.01
tlaxcala	733642.80	134414.00	599228.80	168570.419	441059.10	407526.00
veracruz	1360234.55	569513.00	790721.55	222325.058	967546.76	966462.62
yucatan	160737.44	8924.00	151813.44	40031.634	109519.68	120614.85
zacatecas	2689221.34	221965.00	2467256.34	508583.271	588443.59	389535.00

maize %>% 
  ggplot(aes(state, ag_prod)) +
  geom_boxplot() +
  ylab("Production (tonnes)") +
  xlab("State") +
  #scale_y_continuous(labels = comma) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust= 0.5))

## Warning: Removed 68 rows containing non-finite values (stat_boxplot).

number_obs <- maize %>% 
  group_by(state) %>% 
  summarise(obs = sum(!is.na(ag_prod)))

## `summarise()` ungrouping output (override with `.groups` argument)

maize_complete <- number_obs %>% 
  filter(obs > 34)
maize_complete

## # A tibble: 31 x 2
##    state                 obs
##    <chr>               <int>
##  1 aguascalientes         35
##  2 baja california sur    35
##  3 campeche               35
##  4 chiapas                35
##  5 chihuahua              35
##  6 coahuila               35
##  7 colima                 35
##  8 distrito federal       35
##  9 durango                35
## 10 guanajuato             35
## # … with 21 more rows

maize_ts <- maize %>% 
  ggplot(aes(year, ag_prod)) + 
  geom_line()+
  ylab("Production (tonnes)") +
  xlab("Years") +
  ggtitle("maize Production 1980 - 2016") +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  geom_rect(data = subset(maize, state %in% c(maize_complete$state)), 
            fill = NA, colour = "red", xmin = -Inf,xmax = Inf,
            ymin = -Inf,ymax = Inf) +     
  facet_wrap(~state, scales="free_y", ncol=5) 
  #facet_wrap(~state, ncol=5)
maize_ts  

Yield

maize %>% 
  group_by(state) %>% 
  summarise(max_yield = max(ag_yield, na.rm=T),
            min_yield = min(ag_yield, na.rm=T),
            range_yield = max(ag_yield, na.rm=T) - min(ag_yield, na.rm=T),
            sd_yield = sd(ag_yield, na.rm=T),
            mean_yield = mean(ag_yield, na.rm=T),
            median_yield = median(ag_yield, na.rm=T)) %>% 
  knitr::kable()

## `summarise()` ungrouping output (override with `.groups` argument)

state	max_yield	min_yield	range_yield	sd_yield	mean_yield	median_yield
aguascalientes	39.87	2.00	37.87	7.2798377	11.8245714	10.59
baja california	38.54	0.98	37.56	8.5605573	7.2329032	3.65
baja california sur	7.41	2.64	4.77	1.3394085	5.2271429	5.24
campeche	2.74	0.58	2.16	0.5573117	1.5691429	1.56
chiapas	2.56	1.45	1.11	0.2942522	1.9654286	1.90
chihuahua	9.97	0.57	9.40	2.4569107	5.1268571	4.85
coahuila	18.86	1.07	17.79	3.7133300	5.1442857	4.61
colima	10.51	1.76	8.75	2.1389538	4.0705714	3.12
distrito federal	11.00	1.97	9.03	1.4986115	2.7760000	2.48
durango	12.23	1.14	11.09	2.4056917	3.8565714	3.48
guanajuato	5.90	1.56	4.34	1.3113085	3.1977143	2.76
guerrero	3.06	0.97	2.09	0.4969693	2.2025714	2.26
hidalgo	3.46	1.56	1.90	0.5767255	2.2722857	2.21
jalisco	10.60	3.16	7.44	2.0650197	6.1022857	5.63
mexico	6.51	2.55	3.96	1.0881238	4.3457143	4.56
michoacan	4.15	1.46	2.69	0.7386203	2.5660000	2.31
morelos	3.50	0.91	2.59	0.7066578	2.3077143	2.10
nayarit	8.78	1.91	6.87	1.6842245	3.7422857	2.95
nuevo leon	11.06	1.00	10.06	2.2887925	3.3737143	2.64
oaxaca	1.54	0.92	0.62	0.1389323	1.2651429	1.26
puebla	2.79	1.13	1.66	0.3887487	1.9354286	1.99
queretaro	15.83	0.95	14.88	3.1923000	4.7117143	3.86
quintana roo	0.99	0.26	0.73	0.1616054	0.6131429	0.60
san luis potosi	1.75	0.81	0.94	0.2563096	1.2337143	1.18
sinaloa	10.54	1.03	9.51	3.0795467	5.7425714	6.14
sonora	7.05	2.77	4.28	1.1458762	4.6897143	4.69
tabasco	2.35	1.19	1.16	0.1957906	1.6331429	1.62
tamaulipas	4.94	1.56	3.38	0.9109864	2.9554286	2.74
tlaxcala	5.66	1.69	3.97	1.3930423	3.4051429	2.69
veracruz	2.44	1.47	0.97	0.2583350	1.8357143	1.84
yucatan	1.71	0.49	1.22	0.1963811	0.8885714	0.89
zacatecas	8.98	0.74	8.24	1.7751668	2.0965714	1.34

maize %>% 
  ggplot(aes(state, ag_yield)) +
  geom_boxplot() +
  ylab("Yield (tonnes/ha)") +
  xlab("State") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust= 0.5))

## Warning: Removed 68 rows containing non-finite values (stat_boxplot).

number_obs <- maize %>% 
  group_by(state) %>% 
  summarise(obs = sum(!is.na(ag_yield)))

## `summarise()` ungrouping output (override with `.groups` argument)

maize_complete <- number_obs %>% 
  filter(obs > 34)
maize_complete

## # A tibble: 31 x 2
##    state                 obs
##    <chr>               <int>
##  1 aguascalientes         35
##  2 baja california sur    35
##  3 campeche               35
##  4 chiapas                35
##  5 chihuahua              35
##  6 coahuila               35
##  7 colima                 35
##  8 distrito federal       35
##  9 durango                35
## 10 guanajuato             35
## # … with 21 more rows

maize_ts <- maize %>% 
  ggplot(aes(year, ag_yield)) + 
  geom_line()+
  ylab("Yield (tonnes/ha)") +
  xlab("Years") +
  ggtitle("maize Yields 1980 - 2016") +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  geom_rect(data = subset(maize, state %in% c(maize_complete$state)), 
            fill = NA, colour = "red", xmin = -Inf,xmax = Inf,
            ymin = -Inf,ymax = Inf) +     
  facet_wrap(~state, scales="free_y", ncol=5) 
  #facet_wrap(~state, ncol=5)
maize_ts  

Area

maize %>% 
  group_by(state) %>% 
  summarise(max_area = max(ag_harv, na.rm=T),
            min_area = min(ag_harv, na.rm=T),
            range_area = max(ag_harv, na.rm=T) - min(ag_harv, na.rm=T),
            sd_area = sd(ag_harv, na.rm=T),
            mean_area = mean(ag_harv, na.rm=T),
            median_area = median(ag_harv, na.rm=T)) %>% 
  knitr::kable()

## `summarise()` ungrouping output (override with `.groups` argument)

state	max_area	min_area	range_area	sd_area	mean_area	median_area
aguascalientes	107831.0	11862.00	95969.00	28937.729	68341.029	66869.0
baja california	18469.0	6.50	18462.50	4305.230	3838.984	2510.0
baja california sur	21908.0	1550.00	20358.00	6358.061	7633.316	4981.0
campeche	186293.0	22196.00	164097.00	50751.509	97657.640	104431.0
chiapas	960143.8	504332.00	455811.84	120143.243	746915.560	702700.0
chihuahua	467063.0	78410.00	388653.00	89282.790	257589.847	259377.0
coahuila	54916.0	14750.55	40165.45	11165.860	33169.454	32642.0
colima	43703.0	11920.00	31783.00	10029.121	24591.167	24109.0
distrito federal	14384.0	2160.00	12224.00	3184.903	8485.669	8136.0
durango	235521.0	94009.76	141511.24	36710.573	177580.652	181708.0
guanajuato	455055.6	147933.00	307122.58	77625.786	327466.658	336557.0
guerrero	513566.0	341752.00	171814.00	41750.341	457701.783	466473.0
hidalgo	280490.0	105952.00	174538.00	32671.741	228483.923	233995.2
jalisco	916264.0	590515.49	325748.51	68458.874	738114.661	748010.7
mexico	746816.0	360047.60	386768.40	80984.400	607378.266	605970.5
michoacan	561059.0	355787.48	205271.52	41065.500	458596.557	464983.0
morelos	57820.0	19452.00	38368.00	10091.074	38934.708	39360.0
nayarit	107741.0	43213.25	64527.75	14931.730	61620.539	57382.0
nuevo leon	94674.3	12611.80	82062.50	20017.335	52610.726	54303.0
oaxaca	598942.5	239930.00	359012.52	92999.724	473285.145	485977.3
puebla	629374.0	309824.31	319549.69	75510.784	521914.969	541182.0
queretaro	123355.9	43143.00	80212.90	23555.979	86984.931	88259.0
quintana roo	85575.0	9660.00	75915.00	20425.422	49413.715	49868.0
san luis potosi	218808.1	46456.00	172352.15	47219.335	143351.988	146118.0
sinaloa	613197.3	46119.00	567078.29	180695.804	311682.077	363936.0
sonora	172528.0	15090.00	157438.00	38179.905	51395.407	35458.0
tabasco	105023.0	30880.00	74143.00	18947.972	66039.302	67377.0
tamaulipas	504704.0	98155.25	406548.75	100911.694	213186.951	198205.0
tlaxcala	161104.0	73478.00	87626.00	18408.116	131840.288	131124.0
veracruz	647535.4	338706.00	308829.40	74599.616	522976.031	536845.0
yucatan	166785.0	13128.00	153657.00	40496.663	123131.349	139404.0
zacatecas	442043.0	101513.60	340529.40	79128.893	295477.558	297417.5

maize %>% 
  ggplot(aes(state, ag_harv)) +
  geom_boxplot() +
  ylab("Area (tonnes)") +
  xlab("State") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust= 0.5))

## Warning: Removed 67 rows containing non-finite values (stat_boxplot).

number_obs <- maize %>% 
  group_by(state) %>% 
  summarise(obs = sum(!is.na(ag_harv)))

## `summarise()` ungrouping output (override with `.groups` argument)

maize_complete <- number_obs %>% 
  filter(obs > 34)
maize_complete

## # A tibble: 31 x 2
##    state                 obs
##    <chr>               <int>
##  1 aguascalientes         35
##  2 baja california sur    35
##  3 campeche               35
##  4 chiapas                35
##  5 chihuahua              35
##  6 coahuila               35
##  7 colima                 35
##  8 distrito federal       35
##  9 durango                35
## 10 guanajuato             35
## # … with 21 more rows

maize_ts <- maize %>% 
  ggplot(aes(year, ag_harv)) + 
  geom_line()+
  ylab("Area harvested (ha)") +
  xlab("Years") +
  ggtitle("maize - Area Harvested (ha) 1980 - 2016") +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  geom_rect(data = subset(maize, state %in% c(maize_complete$state)), 
            fill = NA, colour = "red", xmin = -Inf,xmax = Inf,
            ymin = -Inf,ymax = Inf) +     
  facet_wrap(~state, scales="free_y", ncol=5) 
  #facet_wrap(~state, ncol=5)
maize_ts  

Losses

maize %>% 
  group_by(state) %>% 
  summarise(max_losses = max(ag_losses, na.rm=T),
            min_losses = min(ag_losses, na.rm=T),
            range_losses = max(ag_losses, na.rm=T) - min(ag_losses, na.rm=T),
            sd_losses = sd(ag_losses, na.rm=T),
            mean_losses = mean(ag_losses, na.rm=T),
            median_losses = median(ag_losses, na.rm=T)) %>% 
  knitr::kable()

## `summarise()` ungrouping output (override with `.groups` argument)

state	max_losses	min_losses	range_losses	sd_losses	mean_losses	median_losses
aguascalientes	98179.0	838.0	97341.0	26988.5843	34821.7794	32473.500
baja california	3937.0	0.0	3937.0	874.2048	652.6129	327.000
baja california sur	4479.0	0.0	4479.0	758.1018	511.7162	295.750
campeche	102704.0	603.0	102101.0	24488.9392	20843.0917	11390.000
chiapas	120142.0	0.0	120142.0	30016.9511	28319.8903	19373.000
chihuahua	120423.0	0.0	120423.0	37834.4437	43595.8477	31065.000
coahuila	26530.0	4.5	26525.5	7538.9236	12510.2131	12368.000
colima	11297.0	0.0	11297.0	2560.0987	1852.5429	849.500
distrito federal	3083.0	0.0	3083.0	633.4964	334.8771	21.500
durango	116948.0	734.0	116214.0	30675.0632	29733.5677	16725.000
guanajuato	315239.0	5352.0	309887.0	70134.1731	94967.8444	68616.000
guerrero	118839.0	1971.0	116868.0	27630.4202	20315.9821	10258.250
hidalgo	147952.0	4178.9	143773.1	34477.2629	43952.7397	33819.850
jalisco	201237.0	0.0	201237.0	45142.1965	51988.8726	35475.500
mexico	284474.0	0.0	284474.0	56461.4675	31347.6224	13341.000
michoacan	134149.0	6713.0	127436.0	29750.4589	35081.6806	26622.500
morelos	36077.0	0.0	36077.0	6468.7874	2406.3403	133.000
nayarit	11254.0	0.0	11254.0	2878.9156	2337.1486	1261.000
nuevo leon	81924.0	404.0	81520.0	23709.7851	29212.2491	30140.000
oaxaca	187341.0	1010.0	186331.0	51409.7368	65867.4032	43324.500
puebla	292870.2	4240.0	288630.2	74611.8889	79630.7631	48354.000
queretaro	74808.4	0.0	74808.4	20777.9119	25499.7086	18273.000
quintana roo	64672.0	350.0	64322.0	19000.7413	21795.2380	17931.000
san luis potosi	152662.2	37590.0	115072.2	35708.2068	90717.3109	95791.500
sinaloa	416497.8	5046.0	411451.8	71139.0756	47302.9776	27423.500
sonora	24422.0	0.0	24422.0	5689.9027	4264.8015	2649.000
tabasco	34128.0	242.0	33886.0	8200.7811	8798.5000	5850.625
tamaulipas	88012.0	8466.0	79546.0	21456.4533	38187.3582	34609.150
tlaxcala	71459.0	0.0	71459.0	15066.7309	8802.8491	1498.000
veracruz	117580.0	5149.0	112431.0	29292.9785	54707.6020	49240.000
yucatan	171644.0	0.0	171644.0	45117.3412	32991.3453	13879.500
zacatecas	180285.0	1773.0	178512.0	53379.0982	68336.0388	69056.000

maize %>% 
  ggplot(aes(state, ag_losses)) +
  geom_boxplot() +
  ylab("Losses (ha)") +
  xlab("State") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust= 0.5))

## Warning: Removed 83 rows containing non-finite values (stat_boxplot).

number_obs <- maize %>% 
  group_by(state) %>% 
  summarise(obs = sum(!is.na(ag_losses)))

## `summarise()` ungrouping output (override with `.groups` argument)

maize_complete <- number_obs %>% 
  filter(obs > 34)
maize_complete

## # A tibble: 16 x 2
##    state              obs
##    <chr>            <int>
##  1 campeche            35
##  2 chiapas             35
##  3 chihuahua           35
##  4 coahuila            35
##  5 colima              35
##  6 distrito federal    35
##  7 durango             35
##  8 hidalgo             35
##  9 morelos             35
## 10 nayarit             35
## 11 nuevo leon          35
## 12 puebla              35
## 13 queretaro           35
## 14 quintana roo        35
## 15 tlaxcala            35
## 16 veracruz            35

maize_ts <- maize %>% 
  ggplot(aes(year, ag_losses)) + 
  geom_line()+
  ylab("Losses (ha)") +
  xlab("Years") +
  ggtitle("maize - Losses (planted-harvested) (ha) 1980 - 2016") +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  geom_rect(data = subset(maize, state %in% c(maize_complete$state)), 
            fill = NA, colour = "red", xmin = -Inf,xmax = Inf,
            ymin = -Inf,ymax = Inf) +     
  facet_wrap(~state, scales="free_y", ncol=5) 
  #facet_wrap(~state, ncol=5)
maize_ts  

Regression line

maize_lm <- maize %>% 
  ggplot(aes(year, ag_yield)) + 
  geom_point() +
  geom_smooth(method = "lm", se = T) +
  ylab("Yield (tonnes/ha)") +
  xlab("Years") +
  ggtitle("Maize Yields") +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  geom_rect(data = subset(maize, state %in% c(maize_complete$state)), 
            fill = NA, colour = "red", xmin = -Inf,xmax = Inf,
            ymin = -Inf,ymax = Inf) +     
  facet_wrap(~state, scales="free_y", ncol=5) 
maize_lm

## Warning: Removed 68 rows containing non-finite values (stat_smooth).

## Warning: Removed 68 rows containing missing values (geom_point).